• Smart Structures and Systems
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• v.31 no.3
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• pp.247-257
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• 2023

In structural health monitoring of large-scale structures, optimal sensor placement plays an important role because of the high cost of sensors and their supporting instruments, as well as the burden of data transmission and storage. In this study, a vibration sensor placement algorithm based on deep reinforcement learning (DRL) is proposed, which can effectively solve non-convex, high-dimensional, and discrete combinatorial sensor placement optimization problems. An objective function is constructed to estimate the quality of a specific vibration sensor placement scheme according to the modal assurance criterion (MAC). Using this objective function, a DRL-based algorithm is presented to determine the optimal vibration sensor placement scheme. Subsequently, we transform the sensor optimal placement process into a Markov decision process and employ a DRL-based optimization algorithm to maximize the objective function for optimal sensor placement. To illustrate the applicability of the proposed method, two examples are presented: a 10-story braced frame and a sea-crossing bridge model. A comparison study is also performed with a genetic algorithm and particle swarm algorithm. The proposed DRL-based algorithm can effectively solve the discrete combinatorial optimization problem for vibration sensor placements and can produce superior performance compared with the other two existing methods.

• The Journal of the Korea Contents Association
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• v.13 no.12
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• pp.9-18
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• 2013

In recent years, the demands of multimedia data in wireless sensor networks have been significantly increased for the high-quality environment monitoring applications that utilize sensor nodes to collect multimedia data. However, since the amount of multimedia data is very large, the network lifetime and network performance are significantly reduced due to excessive energy consumption on particular nodes. In this paper, we propose an energy-efficient dynamic area compression scheme in wireless multimedia sensor networks. The proposed scheme minimizes the energy consumption in the huge multimedia data transmission process by compression using the Chinese Remainder Theorem(CRT) and dynamic area detection and division algorithm. Our experimental results show that our proposed scheme improves the data compression ratio by about 37% and reduces the amount of transmitted data by about 56% over the existing scheme on average. In addition, the proposed scheme increases network lifetime by about 14% over the existing scheme on average.

• Journal of the Korea Society for Simulation
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• v.29 no.2
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• pp.73-81
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• 2020

HCCL stands for Heterogenous Container Class Library. HCCL is a library that allows heterogeneous types of data to be stored in a container as a single record and to be constructed as a list of the records to be stored in database. With HCCL, encryption/decryption can be done based on the unified data type. Recently, IoT sensor which is embedded in smartphone enables developers to provide various convenient services to users. However, it is also true that infringement of personal information may occur in the process of transmitting sensor information to API and users need to be prepared for this situation in some sense. In this study, we developed a data model that enhances existing security using SEED cryptographic algorithms while managing information of sensors based on HCCL. Due to the fact that the Android environment does not provide permission management function for sensors, this study decided whether or not to encrypt sensor information based on the user's choice so that the user can determine the creation and storage of safe data. For verification of this work, we have presented the performance evaluation by comparing with the situation of storing the sensor data in plaintext.

• Journal of Korea Multimedia Society
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• v.18 no.3
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• pp.408-418
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• 2015

This paper describes the design and implementation of situation recognition system with smart phone sensors, which recognizes the dangerous situation at anytime, anywhere through intuitive data analysis of the combination of the sensor. The implemented system consists of wearable heart rate sensor and acceleration sensor of smart phone instead of existing sensor that is attached to the body. It is also designed to get more effective results of recognition about the dangerous situation using merged displacement values of acceleration sensor and heart rate sensor which are measured in the process of recognizing dangerous situations. This research, in accordance with the wide penetration of smartphones, achieves the fast status determination through the combination of an acceleration sensor and a heart rate sensor applied to its own status perception algorithm for anyone who needs the stable perception of risk without the need for a separate provision of the sensor.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.1581-1584
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• 2003

This paper proposed efficient manufacturing system using OMM(on-machine measurement) system and OMM operating S/W based 3D modeler. A Developed program connected tool machine with RS232C. It is composed two operating system that touch probe operating and laser displacement sensor operating system. A program for touch probe possible measure considered inspection feature and CAD data. The laser operating program is used inspection for profile. very small hole using installed feature data. This system is applied manufacturing line of mold(cavity, core) also verification of efficiency manufacturing process that production, reduction machining error of each process

• ETRI Journal
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• v.41 no.4
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• pp.437-451
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• 2019

Outlier detection techniques play an important role in enhancing the reliability of data communication in wireless sensor networks (WSNs). Considering the importance of outlier detection in WSNs, many outlier detection techniques have been proposed. Unfortunately, most of these techniques still have some potential limitations, that is, (a) high rate of false positives, (b) high time complexity, and (c) failure to detect outliers online. Moreover, these approaches mainly focus on either temporal outliers or spatial outliers. Therefore, this paper aims to introduce novel algorithms that successfully detect both temporal outliers and spatial outliers. Our contributions are twofold: (i) modifying the Hampel Identifier (HI) algorithm to achieve high accuracy identification rate in temporal outlier detection, (ii) combining the Gaussian process (GP) model and graph-based outlier detection technique to improve the performance of the algorithm in spatial outlier detection. The results demonstrate that our techniques outperform the state-of-the-art methods in terms of accuracy and work well with various data types.

• Journal of KIISE:Databases
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• v.36 no.6
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• pp.476-485
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• 2009

In sensor networks, many methods have been proposed to process in-network aggregation effectively. Unlike general aggregation queries, skyline query processing compares multi-dimensional data for the result. Therefore, it is very difficult to process the skyline queries in sensor networks. It is important to filter unnecessary data for energy-efficient skyline query processing. Existing approach like MFTAC restricts unnecessary data transitions by deploying filters to whole sensors. However, network lifetime is reduced by energy consumption for many false positive data and filters transmission. In this paper, we propose a bottom up filtering-based skyline query processing algorithm of in-network for reducing energy consumption by filters transmission and a PBFiltering technique for improving performance of filtering. The proposed algorithm creates the skyline filter table (SFT) in the data gathering process which sends from sensor nodes to the base station and filters out unnecessary transmissions using it. The experimental results show that our algorithm reduces false positives and improves the network lifetime over the existing method.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.2
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• pp.115-123
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• 2021

A lot of sensor and control signals is generated by an industrial controller and related internet-of-things in discrete manufacturing system. The acquired signals are such records indicating whether several process operations have been correctly conducted or not in the system, therefore they are usually composed of binary numbers. For example, once a certain sensor turns on, the corresponding value is changed from 0 to 1, and it means the process is finished the previous operation and ready to conduct next operation. If an actuator starts to move, the corresponding value is changed from 0 to 1 and it indicates the corresponding operation is been conducting. Because traditional fault detection approaches are generally conducted with analog sensor signals and the signals show stationary during normal operation states, it is not simple to identify whether the manufacturing process works properly via conventional fault detection methods. However, digital control signals collected from a programmable logic controller continuously vary during normal process operation in order to show inherent sequence information which indicates the conducting operation tasks. Therefore, in this research, it is proposed to a recurrent neural network-based fault detection approach for considering sequential patterns in normal states of the manufacturing process. Using the constructed long short-term memory based fault detection, it is possible to predict the next control signals and detect faulty states by compared the predicted and real control signals in real-time. We validated and verified the proposed fault detection methods using digital control signals which are collected from a laser marking process, and the method provide good detection performance only using binary values.

• The Journal of the Korea Contents Association
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• v.11 no.2
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• pp.126-134
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• 2011

Recently, many researchers are interested in using join queries to correlate sensor readings stored in different regions. In the conventional algorithm, the preliminary join coordinator collects the synopsis from sensor nodes and determines a set of sensor readings that are required for processing the join query. Then, the base station collects only a part of sensor readings instead of whole readings and performs the final join process. However, it has a problem that incurs communication overhead for processing the preliminary join. In this paper, we propose a novel energy-efficient in-network join scheme that solves such a problem. The proposed scheme determines a preliminary join coordinator located to minimize the communication cost for the preliminary join. The coordinator prunes data that do not contribute to the join result and performs the compression of sensor readings in the early stage of the join processing. Therefore, the base station just collects a part of compressed sensor readings with the decompression table and determines the join result from them. In the result, the proposed scheme reduces communication costs for the preliminary join processing and prolongs the network lifetime.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.227-247
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• 2018

For intermittently connected wireless sensor networks deployed in hash environments, sensor nodes may fail due to internal or external reasons at any time. In the process of data collection and recovery, we need to speed up as much as possible so that all the sensory data can be restored by accessing as few survivors as possible. In this paper a novel redundant data storage algorithm based on minimum spanning tree and quasi-randomized matrix-QRNCDS is proposed. QRNCDS disseminates k source data packets to n sensor nodes in the network (n>k) according to the minimum spanning tree traversal mechanism. Every node stores only one encoded data packet in its storage which is the XOR result of the received source data packets in accordance with the quasi-randomized matrix theory. The algorithm adopts the minimum spanning tree traversal rule to reduce the complexity of the traversal message of the source packets. In order to solve the problem that some source packets cannot be restored if the random matrix is not full column rank, the semi-randomized network coding method is used in QRNCDS. Each source node only needs to store its own source data packet, and the storage nodes choose to receive or not. In the decoding phase, Gaussian Elimination and Belief Propagation are combined to improve the probability and efficiency of data decoding. As a result, part of the source data can be recovered in the case of semi-random matrix without full column rank. The simulation results show that QRNCDS has lower energy consumption, higher data collection efficiency, higher decoding efficiency, smaller data storage redundancy and larger network fault tolerance.